2x3 Matrix Times 3x2 Matrix

Multiplication of ii Matrices in Single line using Numpy in Python

Matrix multiplication is a lengthy process where each element from each row and column of the matrixes are to exist multiplied and added in a sure fashion. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The result matrix has the number of rows of the first and the number of columns of the second matrix.

For smaller matrices nosotros may blueprint nested for loops and observe the result. For bigger matrices we need some built in functionality in python to tackle this. We will run into both approaches below.

Using for Loop

Nosotros take two matrices of dimension 2x3 and 3x2 (rows x columns). The result of matrix multiplication is a 2x2 matrix. Nosotros accept a nested for loop designed to become through the columns of A and rows of B and add the products of the values in those rows and columns.

Example

Live Demo

#matrix A with 2 rows A = ([v,10,xv],[20,25,30])  #matrix B with ii columns B = ([4,8],[12,x],[14,16])  issue = [[0 for x in range(2)] for y in range(ii)]  for i in range(len(A)):    # iterate through columns of A    for j in range(len(B[0])):       # iterate through rows of B       for k in range(len(B)):          result[i][j] += A[i][k] * B[k][j] for r in result:    print(r)

Output

Running the above code gives us the following result:

[350, 380] [800, 890]

Using Numpy

Numpy has a in-built function named dot, that carries out the matrix multiplication. Our number of lines of program becomes very less and the syntax is besides very simple.

Example

Alive Demo

import numpy as np  #matrix A matrix_A = ([5,10,15],[20,25,30])  #matrix B matrix_B = ([4,8],[12,10],[14,xvi]) result = np.dot(matrix_A,matrix_B)  # Result print(effect)